A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation
| Autor: | Zesen Han, Lihong Lai, Zhaokun Pu, Lan Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Cardiovascular Innovations and Applications, Vol 5, Iss 4, p 245 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2009-8782 2009-8618 |
| DOI: | 10.15212/CVIA.2021.0001 |
| Popis: | Objective: To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease (CAD). Methods and Results: A total of 1,025 patients referred for coronary angiography were included in a retrospective, single-center study. Randomly, 720 patients (70%) were selected as the development group and the other patients were selected as the validation group. Multivariate logistic regression analysis showed that the seven risk factors age, sex, systolic blood pressure, lipoprotein-associated phospholipase A 2, type of angina, hypertension, and diabetes were significant for diagnosis of CAD, from which we established model A. We established model B with the risk factors age, sex, height, systolic blood pressure, low-density lipoprotein cholesterol, lipoprotein-associated phospholipase A 2, type of angina, hypertension, and diabetes via the Akaike information criterion. The risk factors from the original Framingham Risk Score were used for model C. From comparison of the areas under the receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement of models A, B, and C, we chose model B to develop the nomogram because of its fitness in discrimination, calibration, and clinical efficiency. The nomogram for diagnosis of CAD could be used easily and conveniently. Conclusion: An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations. The Akaike information criterion is a better method in screening risk factors. The net reclassification improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination. Decision curve analysis can be used to evaluate the efficiency of clinical prediction models. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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